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LifeboatLinux, recover your damaged system from a 35Mb EFI Linux distro
Hacker NewsLifeboatLinux, recover your damaged system from a 35Mb EFI Linux distro
I was tired of having to search for a USB drive every time I mess something with my computer so I decided to create this small EFI file that can be put in your EFI partition to boot from it. It has everything you should ever need to mount your filesystem and chroot to it so you can fix it. You just need to drop the file in your EFI partition and you can boot to it from your BIOS menu. I would appreciate feedback and ideas to make it better.
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Product HuntOn track for Day 1 leaderboard · Strong signals: computer · Missing: mac, agents, macos69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: filesystem, ide, io · Missing: https docs, excited, just released58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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